Learn how synthetic data in healthcare lets medical practitioners share data on clinical trials and drug development without revealing patient identities.
Table of Contents
What is Synthetic Data in Healthcare?
How do Healthcare Providers Use Synthetic Data?
Creating Synthetic Data in Healthcare Scenarios, Step-by-Step
The Challenges of Using Synthetic Data in Healthcare Settings
Synthetic Data in Healthcare Based on Business Entities
Synthetic data in healthcare is artificially created data that emulates actual patient data and is used for a variety of medical treatment and research purposes. By using synthetic patient data, healthcare providers and researchers can utilize and share sensitive medical information to optimize the efficacy of treatment protocols and drug development without compromising patient confidentiality.
The following drivers for synthetic data generation stand out:
Privacy
Privacy regulations like the US Health Insurance Portability and Accountability Act (HIPAA) make it complex and risky to access and share medical information. Healthcare organizations that do so risk not only non-compliance with regulations, but also legal and financial liability.
Scalability
Creating large and diverse real patient datasets can be challenging and may not even be possible in some cases. Synthetic data can be generated at scale, enabling researchers and organizations to access the data they need, when they need it.
Bias mitigation
Biases might be embedded in actual patient data due to demographics, geographic location, and preferences for certain medical facilities. Synthetic data in healthcare can remove biases, or generate datasets with more balanced representations, to ensure more objective and accurate analysis.
Among the many different synthetic data examples, healthcare offers a unique solution. A representative synthetic dataset with the same statistical properties as an actual patient dataset enables safe and effective data sharing, without the liabilities associated with using real data. Leveraging advanced algorithms and statistical methods, synthetic healthcare data is created by tools that extract patterns, relationships, and characteristics from actual patient data, then emulate them. The synthetic data includes the same attributes as the original data, in terms of age distributions, medical conditions, trends, and more.
With synthetic healthcare data, health-focused organizations can overcome data privacy, security, and compliance challenges, while still benefiting from data-driven medical insights. Here are the top 10 ways that healthcare organizations use synthetic data:
Regulatory compliance
Healthcare organizations use synthetic data to comply with data protection regulations and its own data security standards.
R&D
Synthetic data helps research labs develop and test new medical treatments, drugs, and therapies – identifying trends, correlations, and potential outcomes – all without compromising privacy.
Training algorithms and models
A synthetic dataset is invaluable for training Machine Learning (ML) algorithms, ensuring that such models learn from diverse and representative data, without putting privacy at risk.
Testing devices and software
Synthetic test data and test data masking facilitate the testing of medical devices and software, without exposing Personally Identifiable Information (PII).
Medical training and simulation
Healthcare professionals use medical simulations to practice procedures, diagnostics, and treatment plans. With synthetic healthcare data, this is accomplished without actual patient records.
Imaging and diagnostics
For training and evaluating imaging diagnostic algorithms, synthetic data is used to generate fake yet realistic medical images.
Advanced healthcare analytics
Predictive and prescriptive analytics models can help identify and sometimes even prevent potential outbreaks of disease. Researchers use synthetic data to train these models and optimize medical resource allocation.
Population health analysis
A key goal of healthcare organizations is studying population health trends, disease prevalence, and care utilization patterns. Synthetic data in healthcare settings facilitates public health initiatives without revealing patient identities.
Personalized medicine
By simulating patient profiles and responses to various interventions, synthetic data helps healthcare providers create personalized treatment plans.
Data sharing and collaboration
Synthetic data enables healthcare institutions to share their research, insights, and datasets with partners and researchers, while still complying with data protection regulations.
Like synthetic financial data generation, the creation of synthetic data in healthcare settings is a complex process that demands a combination of domain knowledge, statistical expertise, and advanced algorithms. Here's what the process looks like, step-by-step:
Collect and analyze the data
Healthcare organizations first collect and analyze real patient data, such as medical records, lab results, clinical trial findings, and more.
Remove PII
To ensure privacy, all personal and sensitive elements are deleted from the dataset.
Identify patterns
The datasets are analyzed by data scientists to identify key patterns, statistical properties, and attributes – like age distributions, medical conditions, treatment histories, etc.
Subset the data
Data teams can subset the datasets – to train Machine Learning (ML) models via Artificial Intelligence (AI) – and/or employ advanced statistical techniques to create models with the same statistical relationships and dependencies found in the actual dataset.
Generate the synthetic data
Using the latest generative AI synthetic data techniques or the more common rules engine, data cloning, or data masking methods, the data teams generate fake data with new data points that align with the patterns and attributes of the original data.
Validate it
The generated synthetic data is validated to make sure that it accurately represents the original data's characteristics, and any discrepancies or anomalies are addressed.
Test it
The synthetic data is tested to determine its accuracy and efficacy in various use cases.
The use of synthetic data in healthcare settings offers medical researchers and practitioners many benefits, yet also comes with numerous challenges that need to be addressed, such as:
Lack of standardization
There are currently no standardized methods for generating or evaluating synthetic healthcare data, which makes collaboration among various healthcare organizations and research facilities problematic due to lack of data consistency.
Realism and accuracy
It’s hard to generate fake data that accurately aligns with the complexity and diversity of actual patient information. The generated data needs to capture the nuances, rare conditions, and dynamic changes of actual medical records in order to be useful for research and development.
Privacy and anonymity
Even though the primary goal of synthetic data protection of patient privacy, sophisticated re-identification techniques can potentially reverse-engineer synthetic data to reveal personal information – leaving patients exposed and healthcare stakeholders liable.
Ethical usage
Synthetic data in healthcare settings must be used ethically, responsibly, and transparently. Yet ensuring that this happens, and that any insights derived from the data are not misleading or harmful, remains challenging.
When a business entity approach is applied to synthetic data solutions, the result is highly realistic but fake datasets. The business entity (e.g., patient, drug, or clinic) is modeled on metadata automatically discovered from the original data, with referential integrity enforced (by design) across all source systems.
Entity-based synthetic data generation tools leverage a variety of different data generation techniques, used alone or together, including:
Generative AI
Rules engine
Entity cloning
Data masking
Among all the different synthetic data companies,
K2view is the only one to support all 4 techniques.